Automated assessment of blood pressure using BpTRU compared with assessments by a trained technician and a clinic nurse
Bibliographic record
Abstract
OBJECTIVE: To determine the accuracy and reproducibility of a new automated blood pressure manometer (BpTRU) relative to auscultatory blood pressure assessed by a research nurse and to that assessed by a clinic nurse. METHODS: Firefighters in a cohort study had blood pressure assessed on up to five occasions with BpTRU and by a trained research technician. Patients in an internal medicine clinic had blood pressure assessed by the clinic nurse and by BpTRU. The absolute values of blood pressure, reproducibility and effect on hypertension classification were compared with the different methods. RESULTS: The research technician readings were higher than the BpTRU readings at visit 1 (3.0/2.7 mmHg, P<0.0001) but the readings converged by visits 4-5 because of a greater reduction in the research nurse readings. The BpTRU readings had similar reproducibility and classification of hypertension as the research technician but did not exhibit terminal digit preference while the research technician readings did. The BpTRU had substantially lower readings (8/7 mmHg) and fewer hypertensive readings than those of the nurse in the internal medicine clinic. CONCLUSIONS: This preliminary study found that the BpTRU had desirable characteristics that suggest that it would be a suitable replacement for auscultatory assessment of blood pressure in clinical practice. A large confirmatory study performed in a usual clinic setting is required.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".